June 2025 in “Jurnal Bumigora Information Technology (BITe)” In this study, researchers aimed to develop a Naive Bayes algorithm to predict hair loss risk based on personal and clinical data, including age, gender, stress levels, hormones, and family history. Results were not reported.
The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.
January 2021 in “Lecture notes in networks and systems” In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.
February 2023 in “International Journal of Multimedia Computing” In this study, improved hidden Markov algorithms based on Bayesian methods enhanced the resolution and segmentation accuracy of low-dose CT images significantly more than naive Bayesian methods.
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September 2020 in “International journal of computer applications” This study found that the Random Forest machine learning algorithm achieved the highest accuracy, 96%, in diagnosing Polycystic Ovarian Syndrome using patients' clinical data.
